Project Management and Data Collection—A Missing Perspective

Project Management and Data Collection—A Missing Perspective

There are several frameworks that can be used when planning and implementing a project. You might have heard of a few of them—Waterfall, Agile, Scrum, Kanban, Lean, etc. 

However, one of my favorite frameworks isn’t actually on that list. It’s one that was created by Jake Knapp during his time at Google Ventures. 

In his book Sprint, where he lays out the business case for his methodology, Knapp maps out a process called a design “sprint,” which he argues can solve any challenge in five days. 

Unpacking the process

In Knapp’s five-day design sprint, the first day (or 20% of the total time allotted), is entirely devoted to making a plan for solving the problem at hand. 

On this day, the members of a sprint team (ideally, seven people or fewer):

  1. Identify and agree to a long-term goal
  2. Identify any assumptions or challenges that may impact the completion of the goal
  3. Create a map of how customers will interact with a product or solution to reach the identified goal
  4. Revise the map with inputs from domain experts (CX expert, finance expert, marketing expert, etc.) from the company not included in the core team of seven

While solving our clients’ problems in five days is ambitious for the work we do here at Thrive, I apply a variation of this process to all of my project plans. 

In fact, using a product launch we worked on for Tadano America Corporation, I am going to demonstrate our sprint process. 

Real-world application

First, we consulted with Tadano to identify their long-term goals for the project and agreed on a timeframe for completion. 

For this particular product launch, we agreed to create two campaigns—a  tease campaign leading up to the launch; and a launch campaign for the day the product was released. This gave us roughly five months to plan, create, and deploy both campaigns.

Using this timeframe as a starting point, I then created a Gantt chart to map out the project flow between different disciplines— strategy, copywriting, art direction, and design—including who was responsible for each deliverable and the timeframe needed for completion of each phase of the project. 

By mapping out the project flow across each unique discipline, I was able to develop a clearer idea of a number of factors that could impact the project’s outcome. 

For example, when creating the Gantt chart, I had assumed copywriting for the tease campaign would begin a few days before the design phase began. 

Because I identified this assumption before the project started, I was able to identify potential roadblocks which could prevent a smooth pass off between the two disciplines and develop solutions to those issues. Namely, I uncovered that the copywriting team needed much more time and would be working collaboratively with the design team to finesse the headline structure and art direction.   

Additionally, during each weekly resourcing meeting, I adjusted the Gantt chart to ensure that the team capacity the following week was still reasonable and made constant revisions to the project plan to account for changes, developments, and roadblocks.

An added fifth step

The process above gives us a good starting point for a successful project plan. However, we are missing a big piece of the puzzle: data. 

Data is one of the most valuable resources available to us, and it gives us the ability to make informed decisions. Also, remembering the phrase “garbage in, garbage out” is critical to success when it comes to data in general. 

You have to work intentionally to collect and analyze usable, reliable data. Even though we generate gigabytes of data over the course of a project, finding a complete data set for even one project is more difficult than you might expect. 

More often than not, the solution is to take disparate data sets and piece them together to try and approximate a complete data set for analysis and testing. This requires a fair amount of dedicated time and attention. 

I often face this problem when tracking project data at Thrive. So to norm and correct our project data, I’ve added a fifth step to Knapp’s design sprint process that focuses on internal data collection:

Identify key data metrics and confirm methods are in place for data collection

This step ensures that I have checks and balances already in place before a project begins, so that I can collect accurate and complete data throughout the project lifecycle. 

Data types

The vast majority of my internal data collection focuses on metrics for budget tracking (hours completed versus budgeted hours by task, burn-down rates, etc.) and resourcing (demand versus capacity), which are templatized from project to project. 

However, I am constantly revising the templates with new information I gather over the course of a project. With each update, I imagine an ideal future state, and then work backwards to identify what data I will need to get there. 

I ask myself questions such as: 

  • What data will I need to analyze at the end of this phase or project to prove or disprove my hypothesis? 
  • Where will this data be coming from? 
  • Who or where will I need to collect data from? 
  • How can I better set up my data collection process now to be able to have clean data later? 

Proactively thinking about data collection ensures that I feed accurate data into any system I create and sets a reliable baseline for decisions made from that data over the course of the project. 

By the end of step five, I have generated a number of planning artifacts (Gantt chart, budget trackers, etc.) that I use to guide my team through each phase of the project. 

The artifacts will be revised and adjusted over the course of the project as roadblocks appear and are overcome. 

However, by following a planning framework, I know I have created a solid foundation upon which my team can create incredible solutions for our clients.

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